Cross-Lingual Word Sense Disambiguation for
Languages with Scarce Resources
Bahareh Sarrafzadeh, Nikolay Yakovets, Nick Cercone, and Aijun An
Department of Computer Science, York University, Canada
{bahar, hush, nick, aan}@cse.yorku.ca
Abstract. Word Sense Disambiguation has long been a central problem
in computational linguistics. Word Sense Disambiguation is the ability
to identify the meaning of words in context in a computational manner. Statistical and supervised approaches require a large amount of
labeled resources as training datasets. In contradistinction to English,
the Persian language has neither any semantically tagged corpus to aid
machine learning approaches for Persian texts, nor any suitable parallel
corpora. Yet due to the ever-increasing development of Persian pages
in Wikipedia, this resource can act as a comparable corpus for EnglishPersian texts.
In this paper, we propose a cross-lingual approach to tagging the word
senses in Persian texts. The new approach makes use of English sense
disambiguators, the Wikipedia articles in both English and Persian, and
a newly developed lexical ontology, FarsNet. It overcomes the lack of
knowledge resources and NLP tools for the Persian language. We demonstrate the effectiveness of the proposed approach by comparing it to a
direct sense disambiguation approach for Persian. The evaluation results
indicate a comparable performance to the utilized English sense tagger.
Keywords: Word Sense Disambiguation, WordNet, Languages with Scarce
Resources, Cross-Lingual, Extended Lesk, FarsNet, Persian
1
Introduction
Human language is ambiguous, so that many words can be interpreted in multiple ways depending on the context in which they occur. While humans rarely
think about the ambiguities of language, machines need to process unstructured
textual information which must be analyzed in order to determine the underlying
meaning.
Word Sense Disambiguation (WSD) heavily relies on knowledge. Without
knowledge, it would be impossible for both humans and machines to identify the
words’ meaning. Unfortunately, the manual creation of knowledge resources is
an expensive and time consuming effort, which must be repeated every time the
disambiguation scenario changes (e.g., in the presence of new domains, different
languages, and even sense inventories) [1]. This is a fundamental problem which
pervades approaches to WSD, and is called the knowledge acquisition bottleneck.
2
With the huge amounts of information on the Internet and the fact that this
information is continuously growing in different languages, we are encouraged to
investigate cross-lingual scenarios where WSD systems are also needed. Despite
the large number of WSD systems for languages such as English, to date no large
scale and highly accurate WSD system has been built for the Farsi language
due to the lack of labeled corpora and monolingual and bilingual knowledge
resources.
In this paper we propose a novel cross-lingual approach to WSD that takes
advantage of available sense disambiguation systems and linguistic resources for
the English language. Our approach demonstrates the capability to overcome
the knowledge acquisition bottleneck for languages with scarce resources. This
method also provides sense-tagged corpora to aid supervised and semi-supervised
WSD systems. The rest of this paper is organized as follows: After reviewing
related works in Section 2, we describe the proposed cross-lingual approach in
Section 3, and a direct approach to WSD in Section 4; which is followed by
evaluation results and a discussion in Section 5. In Section 6 our concluding
remarks are presented and future extensions are proposed.
2
Related Work
We can distinguish different approaches to WSD based on the amount of supervision and knowledge they demand. Hence we can classify different methods into
4 groups [1]: Supervised, Unsupervised, Semi-supervised and Knowledge-based.
Generally, supervised approaches to WSD have obtained better results than
unsupervised methods. However, obtaining labeled data is not usually easy for
many languages, including Persian as there is no sense tagged corpus for this
language.
The objective of Knowledge-based WSD is to exploit knowledge resources
such as WordNet [2] to infer the senses of words in context. These methods
usually have lower performance than their supervised alternatives, but they have
the advantage of wider coverage, thanks to the use of large-scale knowledge
resources.
The recent advancements in corpus linguistics technologies, as well as the
availability of more and more textual data encourage many researchers to take
advantage of comparable and parallel corpora to address different NLP tasks.
The following subsection reviews some of the related works which address WSD
using a cross-lingual approach.
2.1
Cross-Lingual Approaches
Parallel corpora present a new opportunity for combining the advantages of supervised and unsupervised approaches, as well as an opportunity for exploiting
translation correspondences in the text. Cross-lingual approaches to WSD disambiguate target words by labelling them with the appropriate translation. The
3
main idea behind this approach is the plausible translations of a word in context
restricts its possible senses to a subset [3].
In recent studies [4–7], it has been found that approaches that use crosslingual evidence for WSD attain state-of-the-art performance in all-words disambiguation. However, the main problem of these approaches lies in the knowledge acquisition bottleneck: there is a lack of parallel and comparable corpora
for several languages including Persian - which can potentially be relieved by
collecting corpora on the Web. To overcome this problem, we utilized Wikipedia
pages in both Persian and English. Before introducing our WSD system a brief
survey of WSD systems for the Persian language follows.
2.2
Related Work for Persian
The lack of efficient, reliable linguistic resources and fundamental text processing
modules for the Persian language make it difficult for computer processing. In
recent years there have been two branches of efforts to eliminate this shortage [8].
Some researchers are working to provide linguistic resources and fundamental
processing units. FarsNet [9] is an ongoing project to develop a lexical ontology to
cover Persian words and phrases. It is designed to contain a Persian WordNet in
its first phase and grow to cover verbs’ argument structures in its second phase.
The included words and phrases are selected according to BalkaNet [10] base
concepts and the most frequent Persian words and phrases in utilized corpora.
Therefore, Persian WordNet goes closely in the lines and principles of Princeton
WordNet, EuroWordNet and BalkaNet to maximize its compatibility to these
WordNets and to be connected to the other WordNets in the world to enable
cross-lingual tasks such as Machine Translation, multilingual Information Retrieval and developing multilingual dictionaries and thesauri. FarsNet 1.0 relates
synsets in each POS category by the set of WordNet 2.1 relations. FarsNet also
contains inter-lingual relations connecting Persian synsets to English synsets (in
Princeton WordNet 3.0). [11] exploits an English-Persian parallel corpus which
was manually aligned at the word level and sense-tagged a set of observations
as a training dataset from which a decision tree classifier is learned. [8] devised
a novel approach based on WordNet, eXtended WordNet [12] and verb parts of
FarsNet to extend the Lesk algorithm [13] and find the appropriate sense of a
word in an English sentence. Since FarsNet was not released at the time of publishing this paper, they manually translated a portion of WordNet to perform
WSD for the Persian side. [14] defined heuristic rules based on the grammatical role, POS tags and co-occurrence words of both the target word and its
neighbours to find the best sense.
Others work on developing algorithms with less reliance on linguistic resources. We refer to statistical approaches [15–17] using monolingual corpora for
solving the WSD problem in Farsi texts. Also conceptual categories in a Farsi
thesaurus have been utilized to discriminate senses of Farsi homographs in [18].
Our proposed approach is unique, when compared to most cross-lingual approaches, in the sense that we utilize a comparable corpus, automatically extracted from Wikipedia articles, which can be available for many language pairs
4
even the languages with scarce resources, and our approach is not limited to
sense tagged parallel corpora only. Second, thanks to the availability of FarsNet,
our method tags Persian words using sense tags in the same language instead of
using either a sense inventory of another language or translations provided by a
parallel corpus. Therefore the results of our work can be applied to many monolingual NLP tasks such as Information Retrieval, Text Classification as well as
bilingual ones including Machine Translation and Cross-Lingual tasks. Moreover,
the extended version of the Lesk algorithm has never been exploited to address
WSD for Persian texts. Finally, taking advantage of available mappings between
synsets in WordNet and FarsNet, we were able to utilize an English sense tagger
which uses WordNet as a sense inventory to sense tag Persian words.
3
Introducing the Cross-Lingual Approach: Persian WSD
using Tagged English Words
This approach consists of two separate phases. In the first phase we utilize an English WSD system to assign sense tags to words appearing in English sentences.
In the second phase we transfer these senses to corresponding Persian words.
Since by design these two phases are distinct, the first phase can be considered
as a black box and different English WSD systems can be employed. What is
more, the corresponding Persian words can be Persian pages in Wikipedia or
Persian sentences in the aligned corpus.
We created a comparable corpus by collecting Wikipedia pages which are
available for both English and Persian languages and Persian articles are not
shorter than 250 words. This corpus contains about 35000 words for the Persian
side and 74000 words for English.
Therefore, the Cross-Lingual system contains three main building blocks:
English Sense Disambiguation (first phase), English to Persian Transfer (transition to the second phase) and Persian Sense Disambiguation (second phase).
These components are described in the following sections. Figure 1 indicates the
system’s architecture for the Cross-Lingual section.
3.1
English Sense Disambiguation
As mentioned, different English Sense Disambiguation systems can be employed
in this phase. In this system we utilized the Perl-based application SenseRelate [19] for the English WSD phase. SenseRelate uses WordNet to perform
knowledge-based WSD.
This system allows a user to specify a range of settings to control the desired
disambiguation. We selected the Extended Lesk algorithm which leads to the
most accurate disambiguation [19].
As an input to SenseRelate we provided plain untagged text of English
Wikipedia pages that was preprocessed according to application’s preconditions.
5
Fig. 1. Cross-Lingual System Architecture
We also provided a tweaked stopword list 1 that is more extensive than the one
which came bundled with the application. SenseRelate will tag all ambiguous
words in the input English texts using WordNet as a sense repository.
3.2
English to Persian Transfer
Running SenseRelate for input English sentences, we have English words tagged
with sense labels. Each of these sense labels corresponds to a synset in WordNet containing that word in a particular sense. Most of these synsets have been
mapped to their counterparts in FarsNet. In order to take advantage of these
English tags for assigning appropriate senses to Persian words, first we transfer
these synsets from English to Persian using interlingual relations provided by
FarsNet. As FarsNet is mapped to WordNet 3.0 there are two inter-lingual relations; equal-to and near-equal-to between FarsNet and WordNet synsets. Due to
the relatively small size of FarsNet we used both relations and did not distinguish
between them.
Exploiting these mappings, we match each WordNet synset which is assigned
to a word in an English sentence to its corresponding synset in FarsNet. For this
part, we developed a Perl-based XML-Parser and integrated the results into the
output provided by SenseRelate.
Along with transferring senses, we also need to transfer Wikipedia pages
from English to Persian. Here, we choose the pages which are available in both
languages. Hence we can work with the pages describing the same title in Persian.
1
The initial list is available at http://members.unine.ch/jacques.savoy/clef/persianST.txt
which was modified and extended according to the application requirements.
6
3.3
Persian Sense Disambiguation
There are two different heuristics for disambiguating senses [1]:
– one sense per collocation: nearby words strongly and consistently contribute
to determine the sense of a word, based on their relative distance, order, and
syntactic relationship;
– one sense per discourse: a word is consistently referred with the same sense
within any given discourse or document;
The first heuristic is applicable to any available parallel corpus for English Persian texts, and we can assign the same sense as the English word to its translation
appearing in the aligned Persian sentence. In this case, we obtain a very high
accuracy, although our system would be limited to this specific type of corpus.
Alternatively, since parallel corpora are not easy to obtain for many language
pairs, we utilize Wikipedia pages which are available in both English and Farsi as
a comparable corpus. We used these pages in order to investigate the performance
of our system on such corpus which is easier to collect for languages with scarce
resources.
Note that although Farsi pages are not the direct translation of English pages,
the context is the same for all corresponding pages, which implies many common
words appear in both pages. Consequently, we can assume domain-specific words
appear with similar senses in both languages.
Based on the second hypothesis as the context of both texts is the same, for
each matched synset in FarsNet which contains a set of Persian synonym words,
we find all these words in the Persian text and we assign the same sense as the
English label to them. Since there may be English words which occurred multiple
times in the text and they could receive different sense tags from SenseRelate, we
transfer the most common sense to Persian equivalences. Here we can use either
the “most frequent” sense provided by WordNet as the “most common” sense or
choose the most local frequent sense (i.e., in that particular context). Since the
second heuristic is more plausible we opted to apply the most frequent sense of
each English word in that text to its Persian translations. As an example consider
SenseRelate assigned the second sense of the noun “bank ” to this word in the
following sentence: “a bank is a financial institution licensed by a government.”
and this sense is the most frequent sense in this English article. The Persian
equivalent noun (i.e., “bank ”) has six different senses. Among them we select
the sense which is mapped to the second sense of word bank in WordNet and we
assign this sense from FarsNet to “bank ”.
We consider 3 possible scenarios:
1. An English word has more than one sense, while the equivalent Persian word
only has one sense. So, SenseRelate disambiguates the senses for this English
word, and the equivalent Persian word does not need disambiguation. For
example “free” in English is a polysemic word which can mean both “able
to act at will” and “costing nothing”, while we have different words for these
senses in Persian (“azad” and “majani” respectively). In this case we are
7
confident that the transferred sense must be the correct sense for the Persian
word.
2. Both the English and the Persian words are polysemous, so as their contexts
are the same, the senses should be the same. In this case we use mappings
between synsets in WordNet and FarsNet. For example, the word “branch”
in English and its Persian equivalent “shakheh” both are polysemous with
similar set of senses. For example, if SenseRelate assigned the 5th sense (i.e
“a stream or river connected to a larger one”) of this word to its occurrence
in an English sentence, the mapped synset in FarsNet would also correspond
to this sense of the Persian “shakheh”.
3. The third scenario happens when an English word only has one sense, while
the Persian equivalent has more than one. In this case, as the context of both
texts are the same, the Persian word is more likely to occur with the same
sense as the English word. For example the noun “Milk” in English has only
one meaning, while its translation in Farsi (i.e., “shir”) has three distinct
meanings: milk, lion and (water) tap. However, since SenseRelate assigns a
synset with this gloss “a white nutritious liquid secreted by mammals and
used as food by human beings” to this word, the first sense will be selected
for “shir”.
In summary, for all 3 possible scenarios we utilize the mappings from WordNet
synsets to FarsNet ones. However, according to our evaluation results, the first
case usually leads to more accurate results and the third case results in the
lowest accuracy. Nontheless, when it comes to domain-specific words, all three
cases result in a high precision rate.
4
Direct Approach: Applying Extended Lesk for Persian
WSD
Thanks to the newly developed FarsNet, the Lesk method (gloss overlap) is applicable to Persian texts as well. Since it is worthwhile to investigate the performance of this Knowledge based method - which has not as yet been employed for
disambiguating Persian words - and compare the results of both Cross-Lingual
and Direct approaches, in the second part of this experiment, the Extended Lesk
algorithm has been applied directly to Persian.
4.1
WSD using the Lesk Algorithm
The Lesk algorithm uses dictionary definitions (gloss) to disambiguate a polysemous word in a sentence context. The original algorithm counts the number
of words that are shared between two glosses. The more overlapping the glosses
are, the more related the senses are. To disambiguate a word, the gloss of each of
its senses is compared to the glosses of every other word in a phrase. A word is
assigned to the sense whose gloss shares the largest number of words in common
with the glosses of the other words.
8
The major limitation to this algorithm is that dictionary glosses are often
quite brief, and may not include sufficient vocabulary to identify related senses.
An improved version of the Lesk Algorithm - Extended Lesk [20] - has been
employed to overcome this limitation.
4.2
Extended Gloss Overlap
Extended Lesk algorithm extends the glosses of the concepts to include the
glosses of other concepts to which they are related according to a given concept
hierarchy.
Synsets are connected to each other through explicit semantic relations that
are defined in WordNet. These relations only connect word senses that are used
in the same part of speech. Noun synsets are connected to each other through
hypernym, hyponym, meronym, and holonym relations. There are other types of
relations between different part of speeches in WordNet, but we focused on these
four types in this paper. These relations are also available for Persian synsets in
FarsNet.
Thus, the extended gloss overlap measure combines the advantages of gloss
overlaps with the structure of a concept hierarchy to create an extended view of
relatedness between synsets.
4.3
Applying Extended Lesk to Persian WSD
In order to compare the results of Direct and Cross-Lingual approaches, the
output from the Cross-Lingual phase is used as an input to the knowledge based
(direct) phase. Each tagged word from the input is considered as a target word to
receive the second sense tag based on the extended Lesk algorithm. We adopted
the method described in [20] to perform WSD for the Persian language. Persian
glosses were collected using the semantic relations implemented for FarsNet.
STeP-1 [21] was used for tokenizing glosses and stemming the content words.
5
5.1
Evaluation
Cross-Lingual Approach
The results of this method have been evaluated on comparable English and Persian Wikipedia pages. Seven human experts - who are all native Persian speakers
- were involved in the evaluation process; they evaluated each tagged word as
“the best sense assigned”, “almost accurate” and “wrong sense assigned”. The
second option considers cases in which the assigned sense is not the best available sense for a word in a particular context, but it is very close to the correct
meaning (not a wrong sense) which is influenced by the evaluation metric proposed by Resnik and Yarowsky in [22]. Currently the tagged words from each
Wikipedia article were evaluated by one evaluator only. Evaluation results indicate an error rate of 25% for these pages. Table 1 summarizes these results. Our
9
results indicate that the domain-specific words which usually occur frequently in
both English and Persian texts are highly probable to receive the correct sense
tag.
Table 1. Evaluation Results
Cross-Lingual
Direct
P
R F-Score P
R F-Score P
Best Sense
68%
Almost Accurate 7% 0.35
Wrong Sense
25%
0.48
51%
9% 0.35
40%
0.44
Baseline
R F-Score
39%
8% 0.35
53%
0.40
Due to the relatively smaller size of Persian texts, this system suffers from a
low recall of 35%. However, as Wikipedia covers more and more Persian pages
every day, soon we will be able to overcome this bottleneck.
According to the evaluation results, our Cross-Lingual method gained an Fscore2 of 0.48 which is comparable to 0.54 F-score of SenseRelate using Extended
Lesk [19]. This indicates the performance of our approach can reach the F-score
of the utilized English tagger. Employing a more accurate English sense tagger
thus improves the WSD results for Persian words by far.
This system can be further evaluated by comparing its output to the results
of assigning either random senses or the first sense to words. Since the senses
in FarsNet are not sorted based on their frequency of usage (as compared to
WordNet), we decided to use the first sense appearing in FarsNet (for each
POS). Assigning the first sense to all tagged Persian words, the performance
decreased significantly in terms of accuracy. The results in Table 1 indicate
that, applying our novel approach results in a 28% improvement in accuracy
in comparison with this selected baseline. However, assigning the most frequent
sense to Persian words would be a more realistic baseline which yields a better
estimation for our system’s performance. Thus by the time the frequency of
usage is provided for FarsNet senses, we anticipate that this problem will be
minimized.
5.2
Direct Knowledge-based approach
As mentioned, the output of the Cross-lingual method was tagged again using
the Direct approach. Overall, 53% of the words received a different tag using the
Direct approach. Table 1 indicates the evaluation results for this approach.
2
F-Score is calculated as 2 (1−ErrorRate)·Recall
, where ErrorRate is the percentage of
1−ErrorRate+Recall
words that have been assigned the wrong sense.
10
5.3
Comparison: Knowledge based vs. Cross-Lingual
Both systems employ the Extended Lesk algorithm. While the Cross-Lingual
method applies Extended Lesk on the English side and transfers senses to Persian words, the Direct approach works with Persian text directly. In other words,
the former considers the whole text as the context and assigns one sense per discourse and the latter considers surrounding words and assigns one sense per
collocation. Furthermore, the Cross-Lingual method exploits WordNet for extending the glosses which covers more words, senses and semantic relations than
FarsNet which is employed by the Direct method.
The main advantage of the Cross-Lingual method is that we can utilize any
highly accurate English sense disambiguator for the first phase while the Persian
side remains intact.
On the other hand, this approach assigns the same tag (the most common
sense) to all occurrences of a word which sacrifices accuracy. Moreover, if there
is no English text with the same context available for a Persian corpus, this
method cannot be applied. However collecting comparable texts over the web is
not difficult. Finally, when the bilingual texts are not the direct translation of one
another the system coverage will be limited to common words in both English
and Persian texts. So, Cross-Lingual method mainly works well for domain words
and not for all the words appearing in the Persian texts.
Although Persian WSD while working with Persian texts directly seems to
be more promising the evaluation results indicate a better performance for the
Cross-Lingual system. The reasons for this observation have been investigated
and are as follows:
1. Lack of reliable NLP tools for the Persian language. While STeP-1 has just
been made available as a tokenizer and a stemmer, there is no POS tagger
for Persian which complicated the disambiguation process.
2. Lack of comprehensive linguistics resources for the Persian language. FarsNet
is a very valuable resource for the Persian language. However it is still at a
preliminary stage of development and does not cover all words and senses
in Persian. In terms of size it is significantly smaller (10000 synsets) than
WordNet (more than 117000 synsets) and it covers roughly 9000 relations
between both senses and synsets.
3. More ambiguity for Farsi words. Disambiguating a Farsi word is a big challenge. Due to the fact that the short vowels are not written in the Farsi
prescription, one needs to consider all types of homographs including heteronyms and homonyms. Moreover, there is no POS tagger to disambiguate
Farsi words which dramatically increases the ambiguity for many Farsi words.
6
Conclusion and Future Work
A large number of WSD systems for widespread languages such as English is
available. However, to date no large scale and highly accurate WSD system
11
has been built for the Farsi language due to the lack of labeled corpora and
monolingual and bilingual knowledge resources.
In this paper we overcame this problem by taking advantage of English sense
disambiguators, availability of articles in both languages in Wikipedia and the
newly developed lexical ontology, FarsNet, in order to address WSD for Persian.
The evaluation results of the Cross-lingual approach show a 28% improvement in
accuracy in comparison with the first-sense baseline. The Cross-Lingual approach
performed better than the knowledge based approach which is directly applied
to Persian sentences. However, one of the main reasons for this performance is
that the lack of NLP tools and comprehensive knowledge resources for Persian
introduces many challenges for systems investigating this language.
This paper in the first step examined a novel idea for cross-lingual WSD in
terms of plausibility, feasibility and performance. The ultimate results of our
approach demonstrate a comparable performance to the utilized English sense
tagger. Therefore, in the next step we will replace SenseRelate with another
English sense tagger with a higher F-score. Gaining higher accuracy and recall
for the Persian WSD system we can exploit it as a part of a bootstrapping system
to create the first sense tagged corpus to aid supervised WSD approaches for
the Persian language. Finally, as the available tools and resources improve for
the Persian language, the Direct approach can be employed to address WSD for
Persian texts directly when there is no comparable English text is available.
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